Abstract

Distributed source coding (DSC) has been proven in theory that it can be used to compress correlated signals with or without loss. Recently this coding method has been used for the application of remote signal estimation in wireless sensor networks (WSN), where multiple sensor nodes compress their correlated observations without inter-node communications. Energy and bandwidth are therefore efficiently saved. Challenges remain, however, in the design of practical and adaptive DSC schemes for WSN. In this paper, we study the problem of a random-binning based DSC scheme for remote source estimation in WSN. We design a DSC scheme and analyze its performance on the estimated signal to distortion ratio (SDR), in which observation noise, quantization distortion, DSC decoding errors and network packet losses are all taken into account. With the introduction of a detailed power consumption model for wireless sensor communications, we quantitatively analyze the overall network energy consumption. We further propose a novel adaptive control mechanism for the DSC scheme, which flexibly optimizes the DSC performance in terms of either SDR or energy consumption by adapting the source coding and transmission parameters to the network conditions. Simulations show the proposed DSC scheme and adaptive control mechanism can either save up to 31.6% energy consumption without decreasing the SDR or maximize the SDR with saving up to 9.4% energy consumption.